CN107464005A - Expanded path planning method for vehicle reservation - Google Patents
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Abstract
The invention relates to an expanded path planning method for vehicle reservation. Providing an improved genetic algorithm to solve the new vehicle path planning expansion model, and considering an expansion path planning method of customer movement and road traffic limitation, aiming at reducing the time of taking the vehicle by the customer, establishing a path planning model which is movable by the customer in an appointment mode, wherein the path planning model solves the matching of triples of passengers, vehicles and meeting nodes, and adopts the improved genetic algorithm to solve the model; secondly, decision reference can be provided for company vehicle distribution providing taxi taking service, so that the operating cost is saved; and has the advantages of stability and higher efficiency.
Description
Technical field
The present invention relates to the moveable vehicle path planning of client under vehicle path planning, more particularly to dynamic reservation protocol
Expansion problem, to solve the problems, such as that vehicle cooperates with Transport route planning with what user-to-user information was shared.
Background technology
It is all quiet to be concentrated mainly on each demand point in the planning of worldwide vehicle and its research of expansion problem at present
In the case of only, vehicle route is planned by region division, road cutting, empirical path etc., vehicle is sent with charge free for each node.These
Method does not all account for the moveable situation of client, is differed farther out with the present invention.
In addition, the research of related vehicle path planning expansion problem includes both at home and abroad, Laporte G et al., it is proposed that
One typical VRP expands problem, is called the path planning problem with time window, solves on different delivery place implementation roads
The remarkable algorithm of preset time window can not be exceeded during by planning.Rodrigue et al. devises a kind of vehicle path planning optimization and calculated
Method so that Products have saved 5% cost in transportation.Mester D et al. propose the path rule of limited ability
Draw problem, for path planning problem, add the limitation of transporting power between vehicle and client, by it is various about
Beam, summarise the vehicle path planning that Most current is likely encountered and expand problem.
The existing Patents with vehicle path planning and its expansion problem are concentrated mainly on the son in each special field
System is realized and the design of optimized algorithm.A kind of patent of invention [1], it is proposed that planning driving path planning side for automobile navigation
Method, including road cutting system, running time computing system and planning driving path optimization system, are gathered around when occurring traffic in driving path
When stifled, traffic accident, road closure, real-time traffic changes, and path planning is most short to select again at once
Path.A kind of patent of invention [2] authorized in 2016, there is provided vehicle path planning side based on experience route
Method, the experience of driver is dissolved into path planning, can solve the problem that common planning can not solve the problems, such as.With path link
The related patent [3] of row limitation, there is provided a kind of vehicle path planning method of road limitation, can evade that no through traffic
Road, and can consider limitation passing road cost, cook up the route for meeting user's request.Mainly description will for patent [4]
Vehicle running region is divided at least one subregion, calculates the vehicle running path in per sub-regions to be optimal matching
Method.
[1] a kind of planning driving path planing method for automobile navigation, the application number/patent No.：CN201611245421.6,
Invention designer：Fu Yunfei；Hu Qian；Li Yining；
[2] a kind of vehicle path planning method based on experience route, the application number/patent No.：CN201610417918.5, invention
Designer：Yang Yang；Red legend surpasses；Li Bing；Zhang Dexin；Yan Jianjie；
[3] a kind of vehicle path planning method of road limitation, the application number/patent No.：CN201610417936.3, invention
Designer：Yang Yang；Zhang Dexin；Li Bing；Red legend surpasses；Yan Jianjie；
[4] a kind of method and system of vehicle path planning, the application number/patent No.：CN201611010058.X, invention design
People：Tang Jingren；Qin Hengle.
Existing vehicle path planning and its research for expanding problem are all to discuss, road network whole irremovable in client
All it is the road network of vehicle wheeled, the demand of calling a taxi of actual life can not be met.Such as the vehicle route rule based on experience route
The experience of driver, has mainly been dissolved into path planning by the method for drawing, and can show common rail, experience route, but work as
When there is the road network that vehicle can not travel, this method can not obviously use.It is existing to consider what road limited
Vehicle path planning method mainly considers how to evade the road that no through traffic, while considers to limit the cost of passing road, can
To cook up the route for meeting user's request, but this method does not account for the positive shadow that client's movement is brought to path planning
Ring, simply consider how vehicle moves unilaterally, it is impossible to allow client to participate in decision process.
The content of the invention
Current either the expansion Study on Problems of vehicle path planning or the design of optimized algorithm, all do not account for client
Mobility in Vehicle routing problem, based on this, the present invention is directed to the application of calling a taxi gone on a journey in city, there is provided
It is a kind of it can be considered that client movement and road limitation practical expanding type paths planning method, called a taxi the time with reducing client
For target, the moveable path planning model of client under reservation protocol is established.
The present invention devises a kind of Improving Genetic Algorithm and solves the model, and what this model solved is passenger, vehicle and phase
The matching of the triple of node is met, therefore, in the coded system of chromosome, using integer coding mode in order, simultaneously
The new crossover operation of reference particle group and intersection and the variation for introducing randomness.
Finally, the research of the present invention is subjected to emulation experiment, when all subregion vehicle fleet size is appropriate, finds the party
Method can be effectively reduced the time that client calls a taxi, and compensate for the deficiency that existing vehicle path planning expands Study on Problems, preferably
Ground solves the problems, such as the practical expansion of vehicle path planning under dynamic reservation protocol.
What the present invention studied is the practical expansion problem of vehicle path planning under dynamic reservation protocol, reservation protocol call a taxi as
Shown in Fig. 1.Client is moveable in road network in this problem, has for the path planning system of routine
The special meaning of its own.For convenience, this problem is referred to as Reserved Vehicle Routing by we
Problems with Mobility of Customers (RVRPMC), namely the moveable path rule of client under reservation protocol
The problem of drawing.Hypothesis below that we are well-designed, wherein most information are all by Rational Simplification.
(1) background for reservation problem of calling a taxi is arranged in the transportation network in single city, only considers single corresponding bicycle
Reservation protocol.It is fixed in time in a certain small time window, that is to say, that it is personal that we only study an instantaneous vehicle distribution
Suggestion.Transportation demand can be met, i.e., the number of vehicle is more than the number of passenger, and passenger and special train driver are rationality
Sincere people, defer to the matching of model；
(2) cost of transportation includes money and time.Distance of equal value can be converted into vehicle and the time cost of client, so selection
Time cost as index rather than each highway network node to the distance between.The destination of client is that vehicle can reach
's；
(3) passenger and Che reservation situation are only occurred in certain geographic area limitation, engagement point for client and vehicle all
It is the node shared in road network, and ignores the limitation that time window is called a taxi for reservation；
Based on assumed above, the mathematical description of problem is as follows, is variable declaration first：
Whole traffic network is expressed as the figure of two tuples：G：(V (G), E (G))
Wherein, V (G) is vertex set；E (G) is the set on side；
With i-th passenger (customer) of i marks, passenger's total number of persons is m.That is i=1,2 ..., m；
With j mark jth vehicles (supplier), vehicle fleet n.That is j=1,2 ..., n, and n >=m；
WithRepresent feasible node sets of the passenger i in transportation network, and Vi0Represent passenger i start position.DiExpression multiplies
Objective i destination,Represent feasible node sets of the vehicle j in transportation network, and Vj0Represent vehicle j start position.Vk
Expression can pick the set of the point of passenger.Then haveOrder | Vk|=l；
Assuming that whole figure is simple Connected undigraph (path all allows two way, and temporary transient the problem of not considering to go across the road)
G is a weighted graph, wherein E (G) numerical value ce：E ∈ E (G) represent the physical distance between two nodes；
If in the presence of an ordered sequence between point u, v：W=vue1v1e2……ekvvThen claim the road that an a length of d between u, v be present
Footpath,
Current relation between node is represented in the form of adjacency matrix A：
Work as ekRepresent vi, vjDuring the side of link, A={ aij∈A|aij=ek}
Because shortest path first in a network is comparatively ripe, calculated with the shortest path in the network in above-mentioned zone
There is no problem, so, the data that we use come from some time and carved under transportation network situation, the section of passenger and Che in each automatic network
The data of minimum time between point, distance, using the time as measurement, is completely converted into the time by us in processing.
Floyd algorithms are exactly the algorithm of a solution any two points beeline, and the calculating process of its algorithm is as follows：
1) with ce：E ∈ E (G) and the connected state on side build initial distance matrix
2) to k=1,2, V, calculate：
Wherein
ThenIt is exactly arbitrary node i, the beeline between j；
The traveling average speed of vehicle is V1, the average speed of passenger's walking is V2；
With vectorWhereinVj=VkRepresent passenger i to vehicle feasible network common node VkMost
Few time；
Use matrixWhereinVj=VkRepresent vehicle j to passenger's i feasible network common nodes VkMost
Few time；
With vectorWherein Vi=Vk, Vj=DiRepresent common node VkDestination DiThe minimum time.
1st, three components match
The abstract representation that problem is given in the form of three components match is as described below：
Two connected networks of former problem are converted into three point sets：The point set V of passenger's starting pointc={ UiVi0, vehicle traveling
Play point set Vs={ ∪jVj0, the node set V to meetk.Because each passenger has its specific terminal, therefore need not examine
The point set of terminal is considered, as long as given the node that meets, it is possible to immediately arrive at and arrived at most by the result of previous step
Short path and distance；
So former problem just turns to the minimal weight maximum matching problem of three components；
Three new components are G '：(V (G '), E (G ')), wherein,
OrderThe maximum matching M of this three component can be by 0-1 variables xijkxRepresent：
Object function：
The time that above formula is met by people's car in engagement pointWith the time from engagement point to destinationComposition, I
Target be namely based on some constraints, allow the function to reach minimum value,
So we just should while maximum matches, based on following constraint,
Wherein：
Optimize our object function (namely the time for allowing client to complete once to call a taxi is most short)：
2 model solutions
The present invention adds the part thought of particle cluster algorithm on the basis of genetic algorithm, considers the influence of locally optimal solution,
A new Improving Genetic Algorithm (Adaptive Genetic Algorithm), referred to as AGA algorithms are devised,
2.1 chromosome codings decode
The encoding scheme of model depends on the actual conditions of problem, and this solution to model is the ternary of passenger, vehicle and the node that meets
The matching of group, therefore, in the coded system of chromosome, using integer coding mode in order.First, we establish three
Array, respectively arranging passenger's start node, vehicle start node and the node that can be met.According to this three arrays, I
Each passenger, vehicle can be given to be numbered with the node that meets.Arrange, encoded by two digits structure according to the order of passenger
Into first numbering for representing matched vehicle, second represents the numbering for picking node, and obtained chromosome form is such as
Table 1 below：
The chromosome coding of table 1 is illustrated
For a static problem, the total length of above-mentioned coding is regular length 2m.The coding should meet the of all passengers
One does not repeat and is not more than n, and second can repeat and be not more than l.It is meaningful that coding is exchanged between passenger, because
Two passengers, which are represented, for this have exchanged the vehicle of seating and the place picked, on the premise of vehicle running path is full-mesh,
This exchange will not produce invalid code.
Decoding is the inverse operation of coding, it is necessary to calculate corresponding target letter by decoding during algorithm realization is carried out
Whether several values, and solution meet constraints.Coding/decoding method is by numbering the enquiring vehicle starting point in array and meeting
Position of the point in figure.Functional value is directly obtained by the matrix of beeline；
2.2 fitness calculate
Genetic algorithm measures adaptability of the individual for environment using fitness, and individual fitness is bigger, just has bigger
Chance be screened to the next generation,
Understand that the purpose of optimization is on the premise of each passenger can match vehicle according to the object function of matching, minimize
Total time：minΣI, j, k(F(xijk))
The fitness of chromosome is higher, and chromosome is more excellent, and the target function value of representative is more excellent.Due to the target letter of this model
Number is to ask for minimum problems, therefore, we set the fitness of chromosome as：
fit(xijk)=1/F (xijk)
2.3 modified genetic operators design
Genetic manipulation mainly includes initial population generation, selection, intersects and make a variation,
(a) initial population generates
Genetic algorithm initial solution can not individually be solved, but a series of population of solution compositions.First generation initial population is used as
The starting point of change, certain influence can be produced to the effect of subsequent algorithm.The setting of the population scale of initial population is very heavy simultaneously
Will, population scale setting is too small, can cause the diversity deficiency of population, and algorithm is easily trapped into local optimum in search procedure
Solution.If population scale setting is larger, the optimal speed of algorithm may be affected.The size value of usual initial population
For 20-200, when optimizing larger, can according to circumstances take the circumstances into consideration to increase the scale of population, in this experiment, we set kind
Group's size is 100, and iterations was 50 generations.The method generated at random is usually taken in the initial population of genetic algorithm, still, due to
Triple matching in this problem has the limitation that vehicle is taken, and can generate substantial amounts of trivial solution using the method generated at random, drop
The arithmetic speed of low algorithm.Therefore, the initial of algorithm will be used as by the sample repeatedly obtained without the methods of sampling put back to herein
Population,
The generation step of initial population is as follows：
Step 1：Build interim vehicle array temp, the array of vehicle when another temp is equal to coding；
Step 2：Since first passenger, the digital x and one digital y that is not more than l of the random generation one no more than n, if
Temp [x] ≠ -1, then coding x y are added in chromosome, otherwise find a nearest z, meet temp [z] ≠ -1, will
Coding z y are added in chromosome；
Step 3：Repeat the above steps until all passengers encode completion；
(b) selection opertor
Selection opertor is to simulate the natural selection mechanism that nature is selected the superior and eliminated the inferior, and the high chromosome of fitness enters in selected population
Next step genetic manipulation, it is to ensure the crucial operator that population entirety fitness improves constantly.Because the population at individual of this algorithm is equal
For the feasible solution of problem, it is therefore necessary to carry out protecting excellent operation, ensure that the optimal solution in population is necessarily entered during selection
Enter the next generation, to improve convergence of algorithm speed.Selection mechanism uses the conventional mechanism of roulette, and the operator is a kind of random choosing
The method taken, the selected possibility of the bigger individual of fitness is higher, the wheel disc selection in the similar gambling of this selection mode,
Therefore roulette is named as,
The different zones that wheel disc in practice is pointed to by pointer on wheel disc determine selection result, and account for the bigger portion of region area
The possibility for dividing pointer to stop is bigger.It is similar to its, in the roulette selection of genetic algorithm, made respectively by calculating accumulated probability
The value of individual chromosome collectively constitutes the region of one 0 to 1, and selected areas is determined by caused random number,
Selection opertor generation step is as follows：
Step 1：Calculate the fitness fit of each chromosomei, and added up ∑ fiti
Step 2：Calculate the selected Probability p of each chromosomei=fiti/∑fiti
Step 3：By its according to sort from big to small and calculate sequence under cumulative probability
Step 4：The random number of one [0,1] is produced, cumulative probability is selected less than the chromosome of the value according to its size；
(c) crossover operator
The thought of crossover operator is the genetic evolution process in natural imitation circle, and son is produced by the intersection restructuring of parental chromosome
Generation.Filial generation has the good characteristic that very big chance is integrated with parents, turns into the higher individual of fitness.Crossover operator is constantly updated
Population, it is the extremely operator of core in genetic algorithm.This problem imitates basic particle group algorithm, and colony is obtained according to selection opertor
A part of the optimal solution as parent, any item chromosome is all intersected according to probability and the parent, then solves feasibility
Problem,
The step of operator intersects is as follows：
Step 1：Calculate the fitness probability GBEST=max { fit of the optimum individual of populationi/∑fiti}；
Step 2：Calculate crossover probability p=pi/(pi+GBEST)；
Step 3：The random number of one [0,1] is produced, chooses whether to be intersected according to its size；
Step 4：The random number a that two maximums of generation are m1, a2With the random number b of one [0,1], a is exchanged if b≤0.51,
a2Between fragment, otherwise exchange remove a1, a2Between beyond fragment；
Step 5：Examine chromosome in the presence or absence of repeat vehicle, if in the presence of, using the method for selection opertor look for one it is feasible
Coding；
Step 6：Repeat step 2-5 steps are accessed 1 time until all individuals；
(d) mutation operator
Mutation operator simulation is chromosomal variation in science of heredity, i.e. the filial generation appearance phenotype different from father and mother.The meaning of variation
The adopted local search ability for being on the one hand that algorithm can be strengthened, when search has reached feasible solution adjacent domain, pass through change
Exclusive-OR operator carries out local directed complete set, can accelerate to draw close to optimal solution.On the other hand, crossover operator is there is a possibility that algorithm is precocious, nothing
Method converges to more excellent solution.Mutation operator can increase the diversity of population, avoid the occurrence of the phenomenon of immature oils,
Common mutation operation makes a variation including single-point, transposition variation etc..Herein, we are carried out at random using single-point and transposition
Method enters row variation to chromosome.The step of operator makes a variation is as shown in Figure 2：
Step 1：A chromosome is randomly choosed, and generates the random number a of one [0,1],
Step 2：If a≤0.33, two random numbers for being not more than m of generation, exchange passenger's corresponding to the two random numbers
Coding；If 0.33 ＜ a≤0.67, random number of the generation one no more than m and a random number for being not more than n, by first
First coding of passenger corresponding to random number is changed into second random number, then carries out preventing the inspection for repeating vehicle；If
0.67 ＜ a, then a random number no more than m and a random number for being not more than l are generated, by corresponding to first random number
Second coding of passenger is changed into second random number,
Step 3：Repeat step 1-2, number are determined by the product of aberration rate and Population Size.
Compared with existing best technique, the advantage of the invention is that：
1. the present invention realizes the modeling of the moveable path planning problem of client under reservation protocol, go out for the modelling
A kind of one follow-on genetic algorithm is used to solve the Extended Model, can be used in the service of calling a taxi, is user and vehicle both sides
Real-time route planning is provided；
2. the present invention caters to the current market demand, only user does not save call a taxi time and cost, moreover it is possible to is called a taxi clothes for offer
The company of business saves operating cost, and stability is high, the efficiency high of operation.
Brief description of the drawings
The diagram that Fig. 1 reservation protocols are called a taxi；
The idiographic flow of Fig. 2 genetic algorithms variation.
Embodiment
The present invention is directed to the application of calling a taxi gone on a journey in city, there is provided a kind of it can be considered that client's movement and road
The practical expanding type paths planning method of limitation, the time is called a taxi as target to reduce client, establishing client under reservation protocol can
Mobile path planning model.
The present invention devises a kind of Improving Genetic Algorithm and solves the model, and what this model solved is passenger, vehicle and phase
The matching of the triple of node is met, therefore, in the coded system of chromosome, using integer coding mode in order, simultaneously
The new crossover operation of reference particle group and intersection and the variation for introducing randomness.
Claims (5)
1. a kind of expanding type paths planning method for vehicle reservation, consider the expanding type of client's movement and road limitation
Paths planning method, the time is called a taxi as target to reduce client, establish the moveable path planning model of client under reservation protocol,
Characterized in that, the path planning model solves the matching of the triple of passenger, vehicle and the node that meets, lost using modified
Propagation algorithm is specially to model solution：
(1) chromosome coding decodes,
Chromosome coding first, is established three arrays, is arranged passenger respectively and initially saved using integer coding mode in order
Point, vehicle start node and the node that can be met, according to this three arrays, carried out to each passenger, vehicle and the node that meets
Numbering, arranged according to the order of passenger, encode and be made up of two digits, first numbering for representing matched vehicle, the
Two represent the numbering for picking node, and the total length of coding is regular length 2m, and the coding meets first of all passengers not
Repeat and be not more than n, and second is repeatable and is not more than l, wherein：M is passenger's total number of persons, and n is vehicle fleet；
Chromosome coding/decoding method is by numbering the position of enquiring vehicle starting point and engagement point in figure in array, by most
Short-range matrix directly obtains functional value；
(2) fitness is calculated, and adaptability of the individual for environment is measured using fitness, and the fitness of chromosome is higher,
Chromosome is more excellent, and the target function value of representative is more excellent,
(3) genetic operator calculates,
Genetic operator, which calculates, includes population generation, operator selection, operator intersects and operator variation.
2. a kind of expanding type paths planning method for vehicle reservation according to claim 1, it is characterised in that described
The generation step of population is as follows：
Step 2.1：Build interim vehicle array temp, the array of vehicle when another temp is equal to coding；
Step 2.2：Since first passenger, the digital x and one digital y that is not more than l of the random generation one no more than n,
If temp [x] ≠ -1, coding x y are added in chromosome, a nearest z is otherwise found, meets temp [z] ≠ -1,
Coding z y are added in chromosome；
Step 2.3：Repeat the above steps until all passengers encode completion.
3. a kind of expanding type paths planning method for vehicle reservation according to claim 1, it is characterised in that described
Operator selection step is as follows：
Step 3.1：Calculate the fitness fit of each chromosomei, and added up ∑ fiti；
Step 3.2：Calculate the selected Probability p of each chromosomei=fiti/∑fiti；
Step 3.3：By its according to sort from big to small and calculate sequence under cumulative probability
Step 3.4：The random number of one [0,1] is produced, cumulative probability is selected less than the chromosome of the value according to its size.
4. a kind of expanding type paths planning method for vehicle reservation according to claim 1, it is characterised in that described
The step of operator intersects is as follows：
Step 4.1：Calculate the fitness probability GBEST=max { fit of the optimum individual of populationi/∑fiti}；
Step 4.2：Calculate crossover probability p=pi/(pi+GBEST)；
Step 4.3：The random number of one [0,1] is produced, chooses whether to be intersected according to its size；
Step 4.4：The random number a that two maximums of generation are m1, a2With the random number b of one [0,1], exchanged if b≤0.5
a1, a2Between fragment, otherwise exchange remove a1, a2Between beyond fragment；
Step 4.5：Examine chromosome in the presence or absence of repeat vehicle, if in the presence of, using the method for selection opertor look for one can
Row coding；
Step 4.6：Repeat step 4.2~4.5 is accessed 1 time until all individuals.
5. a kind of expanding type paths planning method for vehicle reservation according to claim 1, it is characterised in that described
Operator variation step is as follows：
Step 5.1：A chromosome is randomly choosed, and generates the random number a of one [0,1],
Step 5.2：If a≤0.33, two random numbers for being not more than m of generation, the passenger corresponding to the two random numbers is exchanged
Coding；If 0.33 ＜ a≤0.67, random number of the generation one no more than m and a random number for being not more than n, by first
First coding of passenger corresponding to individual random number is changed into second random number, then carries out preventing the inspection for repeating vehicle；If
0.67 ＜ a, then a random number no more than m and a random number for being not more than l are generated, by corresponding to first random number
Second coding of passenger is changed into second random number,
Step 5.3：Repeat step 5.1-5.2, number are determined by the product of aberration rate and Population Size.
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